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In just over a decade, certified single-junction perovskite solar cells (PSCs) boast an impressive power conversion efficiency (PCE) of 26.1%. Such outstanding performance makes it highly viable for further development. Here, we have meticulously outlined challenges that arose during the industrialization of PSCs and proposed their corresponding solutions based on extensive research. We discussed the main challenges in this field including technological limitations, multi-scenario applications, sustainable development, etc. Mature photovoltaic solutions provide the perovskite community with invaluable insights for overcoming the challenges of industrialization. In the upcoming stages of PSCs advancement, it has become evident that addressing the challenges concerning long-term stability and sustainability is paramount. In this manner, we can facilitate a more effective integration of PSCs into our daily lives.This review summarized the challenges in the industrialization of perovskite solar cells (PSCs), encompassing technological limitations, multi-scenario applications, and sustainable development. Additionally, the focus of future advancements in PSCs was discussed.

Commercialization is widely believed to be achievable for metal halide perovskite solar cells with high efficiency and low fabrication cost. However, stability remains a key obstacle for them to compete with established photovoltaic technologies. The photovoltaic community relies on the International Electrotechnical Commission (IEC) standard for the minimum stability assessment for any commercialized solar cell. In this review, we summarize the main degradation mechanisms of perovskite solar cells and key results for achieving sufficient stability to meet IEC standards. We also summarize limitations for evaluating solar cell stability and commercialization potential within the framework of the current IEC standard, and discuss the importance of outdoor testing. Stable performance in solar cells is a key requirement for industrial success. Here, stability and degradation of perovskite solar cells are discussed within the context of the International Electrotechnical Commission’s standards for commercialized solar cells.

Organometal lead halide perovskites have been widely used as the light harvester for high-performance solar cells. However, typical perovskites of methylammonium lead halides (CH 3 NH 3 PbX 3 , X=Cl, Br, I) are usually sensitive to moisture in ambient air, and thus require an inert atmosphere to process. Here we demonstrate a moisture-induced transformation of perovskite crystals in a triple-layer scaffold of TiO 2 /ZrO 2 /Carbon to fabricate printable mesoscopic solar cells. An additive of ammonium chloride (NH 4 Cl) is employed to assist the crystallization of perovskite, wherein the formation and transition of intermediate CH 3 NH 3 X·NH 4 PbX 3 (H 2 O) 2 (X=I or Cl) enables high-quality perovskite CH 3 NH 3 PbI 3 crystals with preferential growth orientation. Correspondingly, the intrinsic perovskite devices based on CH 3 NH 3 PbI 3 achieve an efficiency of 15.6% and a lifetime of over 130 days in ambient condition with 30% relative humidity. This ambient-processed printable perovskite solar cell provides a promising prospect for mass production, and will promote the development of perovskite-based photovoltaics. The commercialization of solar cells based on hybrid perovskites requires challenges of device stability and scalable production to be addressed. Rong et al . report ambient-processed printable mesoscopic perovskite solar cells with a lifetime of over 130 days in ambient air with 30% relative humidity.

The high power conversion efficiencies of small-area perovskite solar cells (PSCs) have driven interest in the development of commercial devices. Rong et al. review recent progress in addressing stability, how to allow mass production, and how to maintain uniformity of large-area films. They note that lifetimes exceeding 10,000 hours under 1 sun (1 kW/m 2 ) illumination have been reported for printable triple mesoscopic PSCs. Science , this issue p. eaat8235 Perovskite solar cells (PSCs) have witnessed rapidly rising power conversion efficiencies, together with advances in stability and upscaling. Despite these advances, their limited stability and need to prove upscaling remain crucial hurdles on the path to commercialization. We summarize recent advances toward commercially viable PSCs and discuss challenges that remain. We expound the development of standardized protocols to distinguish intrinsic and extrinsic degradation factors in perovskites. We review accelerated aging tests in both cells and modules and discuss the prediction of lifetimes on the basis of degradation kinetics. Mature photovoltaic solutions, which have demonstrated excellent long-term stability in field applications, offer the perovskite community valuable insights into clearing the hurdles to commercialization.

Hyperspectral images (HSIs) contain abundant spectral and spatial structural information, but they are inevitably contaminated by a variety of noises during data reception and transmission, leading to image quality degradation and subsequent application hindrance. Hence, removing mixed noise from hyperspectral images is an important step in improving the performance of subsequent image processing. It is a well-established fact that the data information of hyperspectral images can be effectively represented by a global spectral low-rank subspace due to the high redundancy and correlation (RAC) in the spatial and spectral domains. Taking advantage of this property, a new algorithm based on subspace representation and nonlocal low-rank tensor decomposition is proposed to filter the mixed noise of hyperspectral images. The algorithm first obtains the subspace representation of the hyperspectral image by utilizing the spectral low-rank property and obtains the orthogonal basis and representation coefficient image (RCI). Then, the representation coefficient image is grouped and denoised using tensor decomposition and wavelet decomposition, respectively, according to the spatial nonlocal self-similarity. Afterward, the orthogonal basis and denoised representation coefficient image are optimized using the alternating direction method of multipliers (ADMM). Finally, iterative regularization is used to update the image to obtain the final denoised hyperspectral image. Experiments on both simulated and real datasets demonstrate that the algorithm proposed in this paper is superior to related mainstream methods in both quantitative metrics and intuitive vision. Because it is denoising for image subspace, the time complexity is greatly reduced and is lower than related denoising algorithms in terms of computational cost.

Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.

Organolead halide perovskite materials possess a combination of remarkable optoelectronic properties, such as steep optical absorption edge and high absorption coefficients, long charge carrier diffusion lengths and lifetimes. Taken together with the ability for low temperature preparation, also from solution, perovskite‐based devices, especially photovoltaic (PV) cells have been studied intensively, with remarkable progress in performance, over the past few years. The combination of high efficiency, low cost and additional (non‐PV) applications provides great potential for commercialization. Performance and applications of perovskite solar cells often correlate with their device structures. Many innovative device structures were developed, aiming at large‐scale fabrication, reducing fabrication cost, enhancing the power conversion efficiency and thus broadening potential future applications. This review summarizes typical structures of perovskite solar cells and comments on novel device structures. The applications of perovskite solar cells are discussed. Perovskite solar cells have been advancing rapidly as a new photovoltaic technology. Device structures have been evolving, demonstrating outstanding performance due to the extraordinary optical and electronic features of organolead halide perovskite materials. Typical structures and promising applications of perovskite solar cells are summarized and discussed. This technology appears to be well positioned for commercialization.

Purpose This longitudinal study examined the reciprocal relationships among perceived teacher support, peer support, and student engagement in middle school physical education classes. Methods Using a two-wave panel design, data were collected from 477 Chinese middle school students at two time points: Wave 1 (Time 1, T1) in November 2022 and Wave 2 (Time 2, T1) in May 2023, with a six-month interval representing a typical academic semester. This temporal spacing allows for observing developmental changes while minimizing seasonal effects on physical activity patterns. Participants completed validated measures of teacher support, peer support, behavioral engagement, and emotional engagement. Data were analyzed via cross-lagged structural equation modeling to test bidirectional pathways while controlling for grade and gender. Results (1) Behavioral engagement at T1 positively predicted teacher support ( β  = 0.22, p  < 0.01) and peer support ( β  = 0.19, p  < 0.01) at T2. (2) Emotional engagement at T1 positively predicted teacher support ( β  = 0.22, p  < 0.01) and peer support ( β  = 0.21, p  < 0.01) at T2. (3) Peer support at T1 positively predicted emotional engagement ( β  = 0.16, p  < 0.01) and teacher support ( β  = 0.14, p  < 0.05 for behavioral model; β  = 0.17, p  < 0.01 for emotional model) at T2. Conclusion These findings reveal a spillover effect wherein student engagement fosters subsequent social support, and peer support reinforces both emotional engagement and teacher support. The study underscores the importance of pedagogical strategies that promote active learning and peer interaction, such as cooperative activities and student-centered tasks, to create mutually reinforcing cycles of engagement and support.

Mirror-image dextrocardia is a rare congenital malformation of visceral transposition, and its completely reversed cardiac anatomical structure poses great challenges to cardiac resynchronization therapy (CRT). Currently, clinical experience with left bundle branch pacing (LBBP) in such special patients remains extremely limited. This article reports a case of a 73-year-old male patient diagnosed with mirror-image dextrocardia complicated by advanced heart failure and complete left bundle branch block (CLBBB). Through preoperative mirror-image adaptation of electrocardiogram and digital subtraction angiography (DSA) systems, combined with reverse personalized heat shaping of the delivery sheath during the procedure, we successfully completed the implantation of an LBBP-based cardiac resynchronization therapy defibrillator (CRT-D). This case is extremely rare and provides valuable insights for the management of such patients.